REFERENCES
1. Qiu S, Cai J, Yang Z, et al. Trends in hepatocellular carcinoma mortality rates in the US and projections through 2040. JAMA Netw Open. 2024;7:e2445525.
2. Koshy A. Evolving global etiology of hepatocellular carcinoma (HCC): insights and trends for 2024. J Clin Exp Hepatol. 2025;15:102406.
3. Singal AG, Kanwal F, Llovet JM. Global trends in hepatocellular carcinoma epidemiology: implications for screening, prevention and therapy. Nat Rev Clin Oncol. 2023;20:864-84.
4. Yu H, Kong D, Mo X, et al. MRI-based 2.5D deep learning and radiomics effectively predicted microvascular invasion and Ki-67 expression in hepatocellular carcinoma. PLoS One. 2025;20:e0336579.
5. Xia TY, Zhou ZH, Meng XP, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology. 2023;307:e222729.
6. Barcena-Varela M, Monga SP, Lujambio A. Precision models in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2025;22:191-205.
7. Wang Y, Chi S, Tian Y, et al. Construction of an artificially intelligent model for accurate detection of HCC by integrating clinical, radiological, and peripheral immunological features. Int J Surg. 2025;111:2942-52.
8. Jiang H, Yang C, Chen Y, et al. Development of a model including MRI features for predicting advanced-stage recurrence of hepatocellular carcinoma after liver resection. Radiology. 2023;309:e230527.
9. Xia T, Zhao B, Li B, et al. MRI-based radiomics and deep learning in biological characteristics and prognosis of hepatocellular carcinoma: opportunities and challenges. J Magn Reson Imaging. 2024;59:767-83.
10. Su R, Tao X, Yan L, et al. Early screening, diagnosis and recurrence monitoring of hepatocellular carcinoma in patients with chronic hepatitis B based on serum N-glycomics analysis: a cohort study. Hepatology. 2026;83:40-56.
11. Yu B, Ma W. Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis. Cytokine Growth Factor Rev. 2024;79:29-38.
12. An C, Zuo M, Li W, Wu P. An automated machine-learning model for prognostic risk stratification of intermediate-stage hepatocellular carcinoma after transarterial chemoembolization. Int J Surg. 2025;111:6200-10.
13. van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022;79:102470.
14. Chen X, Wang X, Zhang K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022;79:102444.
15. Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform. 2022;23:bbab569.
16. Davri A, Birbas E, Kanavos T, et al. Deep learning for lung cancer diagnosis, prognosis and prediction using histological and cytological images: a systematic review. Cancers. 2023;15:3981.
17. Yan M, Zhang X, Zhang B, et al. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy. Eur Radiol. 2023;33:4949-61.
18. Wang F, Zhan G, Chen QQ, et al. Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images. Liver Int. 2024;44:1351-62.
19. Lu J, Yao J, Zhang J, et al. SOFT: softmax-free transformer with linear complexity. arXiv 2022;arXiv:2110.11945. Available from https://arxiv.org/abs/2110.11945. [accessed 17 April 2026].
20. Golovneva O, Wang T, Weston J, Sukhbaatar S. Contextual position encoding: learning to count what’s important. arXiv 2024;arXiv:2405.18719. Available from https://arxiv.org/abs/2405.18719. [accessed 17 April 2026].
21. Hwang YJ, Bae JS, Lee Y, Hur BY, Lee DH, Kim H. Classification of microvascular invasion of hepatocellular carcinoma: correlation with prognosis and magnetic resonance imaging. Clin Mol Hepatol. 2023;29:733-46.
22. Altaf A, Mustafa A, Dar A, et al. Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation. Surgery. 2024;176:1500-6.
23. Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021;147:3757-67.
24. Li J, Su X, Xu X, et al. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol. 2023;190:104107.
25. Liu Z, Liu Y, Zhang W, et al. Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatol Int. 2022;16:577-89.
